Input data correction

ABSTRACT

Technical solutions are described that address correcting input time-series data provided for analysis and predictions. An example computer-implemented method includes receiving, by a processor, a time-series data input by a user. The computer-implemented method also includes computing, by the processor, a first plurality of predicted values based on the time-series data input by the user; computing, by the processor, a second plurality of predicted values by. The computer-implemented method also includes determining estimated time-series data based on the time-series data input by the user. The computer-implemented method also includes computing the second plurality of predicted values based on the estimated time-series data. The computer-implemented method also includes determining, by the processor, a defect in the time-series data input by the user based on a distribution of a plurality of differences between respective values from the first plurality of predicted values and the second plurality of predicted values.

BACKGROUND

The present invention generally relates to computer technology, and morespecifically, to detecting error(s) in user-input data, such as in atime-series, and further to identifying a root-cause of the error(s),and in turn to correcting the user-input data.

Computer technology is used to analyze user-input time-series data, suchas observations from a longitudinal study, human/machine health-relateddata, and so on. Typically, the user-input data is multidimensional, andfurther the analysis typically is used to generate data-driven insightsand predictions powered by machine learning and other advancedmathematical models. Generating valid, accurate, and personalizedinsights and predictions is essential for providing value to users andestablishing confidence in the analytical results.

SUMMARY

Embodiments of the present invention are directed to acomputer-implemented method for correcting input time-series data. Anon-limiting example of the computer-implemented method includesreceiving, by a processor, a time-series data input by a user. Thecomputer-implemented method also includes computing, by the processor, apredicted value based on the time-series data. The computer-implementedmethod also includes determining, by the processor, a defect in thetime-series data input by the user based on a difference between thepredicted value and a measured value.

The computer-implemented method further includes determining, by theprocessor, a cause of the defect in the time-series data automaticallyby using a machine learning algorithm. The computer-implemented methodfurther includes displaying, by the processor, a prompt for the user,the prompt displaying the cause of the defect.

The computer-implemented method further includes computing, by theprocessor, an estimated time-series data for the user. Thecomputer-implemented method further includes displaying, by theprocessor, a prompt for the user, the prompt displaying the cause of thedefect and the estimated time-series data to be used instead of thetime-series data input by the user. The computer-implemented methodfurther includes, in response to the user selecting the estimatedtime-series data: computing, by the processor, a revised predicted valuebased on the estimated time-series data. The computer-implemented methodmay also include displaying, by the processor, the revised predictedvalue.

The computer-implemented method further includes determining that thecause of the defect is one from a group of causes including of the userunder-reporting the time-series data, the user over-reporting thetime-series data, and a sensor malfunction.

Embodiments of the present invention are directed to a system forcorrecting input time-series data. A non-limiting example of the systemincludes a memory; and a processor coupled with the memory. Theprocessor receives a time-series data input by a user. The processorfurther computes a predicted value based on the time-series data. Theprocessor further determines a defect in the time-series data input bythe user based on a difference between the predicted value and ameasured value.

In one or more examples, the processor further determines a cause of thedefect in the time-series data automatically by using a machine learningalgorithm. The processor further displays a prompt for the user, theprompt displaying the cause of the defect.

The processor further computes an estimated time-series data for theuser. The processor further displays a prompt for the user, the promptdisplaying the cause of the defect and the estimated time-series data tobe used instead of the time-series data input by the user. The processorfurther, in response to the user selecting the estimated time-seriesdata: compute a revised predicted value based on the estimatedtime-series data. The processor further displays the revised predictedvalue. The processor further determines that the cause of the defect isone from a group of causes including of the user under-reporting thetime-series data, the user over-reporting the time-series data, and asensor malfunction.

Embodiments of the invention are directed to a computer program productfor correcting input time-series data, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith. The program instructions are executableby a processor to cause the processor to perform a method. Anon-limiting example of the instructions cause the processing circuit toreceive a time-series data input by a user. The instructions furthercause the processing circuit to compute a first predicted value based onthe time-series data input by the user. The instructions further causethe processing circuit to compute a second predicted value based on anestimated time-series data, the estimated time-series data computedbased on the time-series data input by the user. The instructionsfurther cause the processing circuit to determine a defect in thetime-series data input by the user based on a difference between thefirst predicted value and the second predicted value.

In one or more examples, the program instructions further cause theprocessing circuit to display a prompt for the user, the promptdisplaying the cause of the defect, and the estimated time-series datato be used instead of the time-series data input by the user. Theprogram instructions are further executable to cause the processingcircuit to determine that the cause of the defect is one from a group ofcauses including of the user under-reporting the time-series data, theuser over-reporting the time-series data, and a sensor malfunction. Inone or more examples, the estimated time-series data is computed usingkalman filtering.

According to one or more embodiments of the present invention acomputer-implemented method includes receiving, by a processor, atime-series data input by a user. The computer-implemented method alsoincludes computing, by the processor, a first plurality of predictedvalues based on the time-series data input by the user; computing, bythe processor, a second plurality of predicted values by. Thecomputer-implemented method also includes determining estimatedtime-series data based on the time-series data input by the user. Thecomputer-implemented method also includes computing the second pluralityof predicted values based on the estimated time-series data. Thecomputer-implemented method also includes determining, by the processor,a defect in the time-series data input by the user based on adistribution of a plurality of differences between respective valuesfrom the first plurality of predicted values and the second plurality ofpredicted values.

In one or more examples, the defect in the time-series is determinedbased on the distribution of the plurality of differences includesdetermining if the distribution is gaussian.

In one or more examples, the computer-implemented method furtherincludes determining, by the processor, a cause of the defect in thetime-series data automatically by using a machine learning algorithmbased on the distribution of the plurality of differences. In one ormore examples, the computer-implemented method further includesdisplaying, by the processor, a prompt for the user, the promptdisplaying the cause of the defect and the estimated time-series data tobe used instead of the time-series data input by the user. In one ormore examples, the computer-implemented method further includes, inresponse to the user selecting the estimated time-series data:computing, by the processor, a revised predicted value based on theestimated time-series data. The computer-implemented method may alsoinclude displaying, by the processor, the revised predicted value.

According to one or more embodiments of the present invention, a systemincludes a memory; and a processor coupled with the memory, where theprocessor receives a time-series data input by a user. The processorfurther computes a first plurality of predicted values based on thetime-series data input by the user; compute a second plurality ofpredicted values by: determining estimated time-series data based on thetime-series data input by the user; and computing the second pluralityof predicted values based on the estimated time-series data. Theprocessor further determines a defect in the time-series data input bythe user based on a distribution of a plurality of differences betweenrespective values from the first plurality of predicted values and thesecond plurality of predicted values.

In one or more examples, the processor further determines a cause of thedefect in the time-series data automatically by using a machine-learningalgorithm based on the distribution of the plurality of differences. Theprocessor further displays a prompt for the user, the prompt displayingthe cause of the defect and the estimated time-series data to be usedinstead of the time-series data input by the user; and in response tothe user selecting the estimated time-series data. The processor furthercomputes a revised predicted value based on the estimated time-seriesdata. The processor further displays the revised predicted value.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 3 depicts a data analysis system for correcting user input data,according to one or more embodiments of the present invention;

FIG. 4 depicts a communication apparatus according to one or moreembodiments of the present invention;

FIG. 5 illustrates a flowchart of an example method for correcting inputdata, according to one or more embodiments of the present invention;

FIG. 6 illustrates a data-flow when implementing the method in anexample scenario, such as the fitness tracking example scenario;

FIG. 7 depicts an example deviation between predicted and measuredvalues;

FIG. 8 illustrates predicting values at future time-points using adynamic system model, according to one or more embodiments of thepresent invention;

FIG. 9 illustrates a flowchart of an example method for determining ifthe input data contains defects, according to one or more embodiments ofthe present invention;

FIG. 10 illustrates a flowchart of an example method for detecting aroot-cause of a defect identified in the input data, according to one ormore embodiments of the present invention;

FIG. 11 depicts an example user-interface generated by the datacorrection apparatus according to one or more embodiments of the presentinvention;

FIG. 12 depicts example user-interface generated by the data correctionapparatus according to one or more embodiments of the present invention;and

FIG. 13 depicts an example user-interface according to one or moreembodiments of the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers. With minorexceptions, the leftmost digit(s) of each reference number correspond tothe figure in which its element is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” may be understood to include any integer numbergreater than or equal to one, i.e. one, two, three, four, etc. The terms“a plurality” may be understood to include any integer number greaterthan or equal to two, i.e. two, three, four, five, etc. The term“connection” may include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

Computer technology is used to analyze user-input time-series data. Inorder to achieve a high degree of accuracy despite inconsistencies inthe data, the one or more embodiments of the present inventionfacilitate detecting defect(s) in input data that affects the analytics.Further, once the defect is detected the one or more embodiments of thepresent invention facilitate identifying a root-cause of the defect.Further yet, the one or more embodiments of the present inventionfacilitate taking remedial actions to correct the erroneous data. Forexample, a user is prompted to correct an erroneous data entry, or thedata is automatically corrected through computational methods.

For example, the one or more embodiments of the present inventionfacilitate detecting, identifying, and correcting errors in user-inputdata regarding fitness tracking. It should be noted that the one or moreembodiments of the present invention are applicable for detecting,identifying, and correcting errors in user-input data related to anyother field, and that fitness tracking data is used as an example todescribe the implementations and applications of one or more embodimentsof the present invention.

For example, the use of computer applications and devices such asMYFITNESSPAL™, FITBIT™, and LOSEIT!™ to track and manage fitness data isincreasing. The data used by such computer program products is typicallyrecorded on multiple dimensions of health and fitness such as dailysteps, nutrition, sleep, and exercise. By tracking fitness and healthdata, users are empowered to analyze patterns in their data to discoverboth healthy and unhealthy behaviors and find where opportunities mayexist for improvement.

Such computer program products, which may be collectively referred to asfitness apps in addition to providing users with simple descriptivestatistics, facilitate generating data-driven insights and predictionspowered by machine learning and other advanced mathematical models. Thestatistical rigor and significance of these insights is dependent on themodels used and on the validity and reliability of the input data usedby the models. Typically, the input data is entered by human users, ormeasured and calculated via consumer-grade devices such as activitytrackers. However, inconsistencies and errors in the data lead theanalytical methods to produce inaccurate predictions and insights.

For example, consider that a computer program product provides aprediction of weight-change over time using statistical methods usingthe input data from a user. However, such statistical methods aresensitive to missing or misreported data and may produce inaccuratepredictions and insights due to such defects in the data. For example, auser that keeps a food diaries can fail to consistently report accuratedaily food intake, either intentionally or accidentally, such as byomitting consumed food items. Furthermore, models used by the computerprogram product to calculate complex metrics such as energy expenditureby physical activity are trained on population-level data and can behighly inaccurate for certain subpopulations and individuals.

Accordingly, the one or more embodiments of the present inventionaddresses the above technical challenges by facilitating generatingvalid, accurate, and personalized insights and predictions, which areessential to providing value to users and establishing confidence in theanalytical results. The one or more embodiments of the presentinvention, in order to achieve a high degree of accuracy despiteinconsistencies in the data, detect the defects in the data that affectthe analytics using differential calibration, and identify theroot-cause of the defects. The one or more embodiments of the presentinvention further facilitate taking remedial actions to correct theerroneous data, for example, prompting a user to correct an erroneousdata entry, or automatically correcting the data through computationalmethods.

In one or more examples, the data from the input data is corrected,prior to or during analyzing the data, by a cloud computing platform. Inone or more examples, the analysis is performed by the same cloudcomputing platform, or a separate cloud computing platform from aseparate cloud computing platform that corrects the input data.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and input data analysis 96.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, typically linear quadraticestimation (LQE) algorithms, such as Kalman filtering and/or machinelearning methods are used for calibrating measurements from inputdevices, optimizing objective functions, or predicting future events forchronically ill patients, collection and management of physiologicaldata from devices for report generation, and intervention planning. Theone or more embodiments of the present invention herein use LQEalgorithms to address the technical problem of defects in the datareceived from consumer-grade health devices that then are used togenerate predictions on fitness and wellness metrics for individualpeople. For example, the one or more embodiments of the presentinvention include methods, systems, computer program products, and otherimplementations for cleaning and processing data, generating predictionsiteratively, and identifying the root-cause of data and predictionerrors.

Additionally, the one or more embodiments of the present inventionfacilitate interaction(s) with a user to address the data defects. Forexample, in case of detected defects, a prompt is displayed to the user,where the user is prompted for input that facilitates the defectdetection and data classification. Further, the one or more embodimentsof the present invention facilitate adaptive data cleaning andpre-processing based on results from the analytics and user feedback.

For example, in case of the fitness tracking devices and computerprogram products, devices and products such as smartphone applicationsand wearable activity trackers record multiple dimensions of data (e.g.food intake, exercise, steps, heart rate, weight, etc.). The input dataincludes one or more time-series data, such as food intake over a periodof one month, one year and so on; exercise over a period of threemonths; etc. The input data from these products are used to generatepredictions and insights for the user, such as weight prediction. Adefect in the input data can be introduced at any point in the processof generating the prediction/insight, for example during datacollection, syncing databases, estimation algorithms, etc. For example,the defect may be introduced because of various root causes, such as avariation in device accuracy depending on use case, omission of inputdata because a user forgets or willfully omits data,under/overestimation by algorithm(s) used, such as to estimate caloricexpenditure. As described herein, these defects in the input data makeit difficult to generate accurate predictions based on the data. Forexample, predicting weight change over time is difficult if data oncaloric intake and expenditure is inaccurate or incomplete.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings bydetecting defects in the data, identifying root-cause(s) of the defects,ameliorating the data defects, and in turn, adjusting predictions tobecome more accurate.

The above-described aspects of the one or more embodiments of thepresent invention address the technical challenges by making thepredictions to be adaptive. For example, one or more predictive modelsin fitness and wellness use static equations and are sensitive to noiseand defects in the data. The one or more embodiments of the presentinvention use data-driven parameter learning that feeds into a dynamicsystem model to produce increasingly accurate predictions that adapt tochanges in the data. Further, the one or more embodiments of the presentinvention facilitate identification of error root-cause to be adaptive.For example, traditional approaches attempt to calibrate and correctdefects, and perform root-cause analysis using rule-based methods foridentifying errors. The one or more embodiments of the present inventionuse classification algorithms to learn the distribution of defectroot-causes from the real user input data. Further yet, the one or moreembodiments of the present invention facilitate integration of userfeedback. For example, results from the root-cause analysis can besurfaced to users to bring attention to the defects in the data entry.Additionally, the user can be prompted to enter verification of datavalidity. Such feedback from the users is ingested into the root-causeanalysis for improving classification accuracy and improving dataimputation.

Turning now to a more detailed description of aspects of the presentinvention, FIG. 3 depicts a data analysis system 100 for correcting userinput data, according to one or more embodiments of the presentinvention. In one or more examples, the data analysis system 100analyzes fitness tracking data for a user 105. Alternatively, or inaddition, the data analysis system 100 analyzes other type(s) of datafor the user 105. In one or more examples, the data analysis system 100is implemented using the cloud computing platform as described herein.For example, the data analysis system 100 includes one or more servercomputers that are remotely accessed over a communication network.Alternatively, or in addition, the data analysis system 100 isimplemented as a local system.

In one or more examples, the data analysis system 100 includes one ormore user-apparatus 110, such as user activity monitors, food intakemonitors, phones, tablet computers, wearable devices, and other suchtype of apparatus that facilitate acquiring corresponding time-series ofinput data that is for analysis. The user-apparatus 110 may include asingle user-apparatus-1 110A, such as a smartphone that the user 105uses to manually enter input data for analysis. Alternatively, or inaddition, the user-apparatus 110 includes multiple devices of differenttypes. For example, the set of user-apparatus 110 includes auser-apparatus-1 110A, a user-apparatus-2 110B, and so on until auser-apparatus-N 110N. In one or more examples, the differentuser-apparatus track corresponding user-activity and/or food consumptionusing one or more sensors, such as a heart-rate monitor, a step-counter,a global position satellite sensor, a gyrometer, and the like.

In one or more examples, each of the user-apparatus 110 forwards thecollected user-activity data for analysis. For example, the data isforwarded to predetermined destination, such as an internet protocol(IP) address, uniform resource locator (URL), or the like. in one ormore examples, the data is additionally stored in a data repository (notshown) that is accessible by other components of the data analysissystem 100. In one or more examples, the data repository is a database.Alternatively, or in addition, the data forwarded by the user-devices110 is analyzed in real-time.

In one or more examples, the data analysis system 100 includes a datainsight apparatus 130 that processes the data to generate a predictionand/or insight. For example, the data insight apparatus 130 predicts aweight-change for the user 105 in case the input data is user activityand/or food consumption time-series data.

The data analysis system 100 further includes a data correctionapparatus 120 that automatically calibrates the user-input data, forexample by detecting, identifying, and correcting defects (e.g. missingdata, erroneous entries, etc.) in the input data. The data correctionapparatus 120 processes the input data prior to the data insightapparatus 130 analyzes the input data. The data correction apparatus120, by processing input data through both rule-based and machinelearning approaches, facilitates the detection and correction of themissing, duplicate, and erroneous input data in an automated manner. Forexample, the data correction apparatus 120 uses dynamic system modelingand LQE algorithms such as Kalman Filtering to learn weights tofacilitate the data insight apparatus 130 to iteratively produceincreasingly accurate predictions that are subjected to noise, errors,and inaccuracies in the data.

Further, the data correction apparatus 120 uses classificationalgorithms to generate probability distributions over possibleroot-causes for the defects in the input data, and also discrepanciesbetween predicted results and the actual results. The data correctionapparatus 120 presents the updated predictions to the user 105.Additionally, the user 105 can be prompted to address possible dataquality issues detected by the root-cause analysis, which can be used toretrain the root-cause classifiers.

Thus, the data correction apparatus 120 facilitates the data analysissystem 100 to generate predictions and/or insights in a more adaptivemanner despite defects in the input data. Further, the data correctionapparatus 120 identifies the root-cause of the defective data in anadaptive manner, and integrates user feedback to correct the defectivedata, further improving accuracy of the predictions/insights.

It should be noted that although FIG. 3 depicts the data correctionapparatus 120 and the insight apparatus 130 as separate boxes, in one ormore examples, the data analysis system may implement the two apparatuson a single machine.

FIG. 4 depicts a communication apparatus 200, according to one or moreembodiments of the present invention. The communication apparatus may bea computer, such as a server, a laptop computer, a tablet computer, aphone, and the like. The communication apparatus 200 may be used as anyone or more of the apparatus depicted in FIG. 3, such as theuser-devices 110, the data correction apparatus 120, the data insightapparatus 130, or a combination thereof.

The communication apparatus 200 includes, among other components, aprocessor 205, memory 210 coupled to a memory controller 215, and one ormore input devices 245 and/or output devices 240, such as peripheral orcontrol devices, that are communicatively coupled via a local I/Ocontroller 235. These devices 240 and 245 may include, for example,battery sensors, position sensors (altimeter 40, accelerometer 42, GPS44), indicator/identification lights and the like. Input devices such asa conventional keyboard 250 and mouse 255 may be coupled to the I/Ocontroller 235. The I/O controller 235 may be, for example, one or morebuses or other wired or wireless connections, as are known in the art.The I/O controller 235 may have additional elements, which are omittedfor simplicity, such as controllers, buffers (caches), drivers,repeaters, and receivers, to enable communications.

The I/O devices 240, 245 may further include devices that communicateboth inputs and outputs, for instance disk and tape storage, a networkinterface card (MC) or modulator/demodulator (for accessing other files,devices, systems, or a network), a radio frequency (RF) or othertransceiver, a telephonic interface, a bridge, a router, and the like.

The processor 205 is a hardware device for executing hardwareinstructions or software, particularly those stored in memory 210. Theprocessor 205 may be a custom made or commercially available processor,a central processing unit (CPU), an auxiliary processor among severalprocessors associated with the communication apparatus 200, asemiconductor based microprocessor (in the form of a microchip or chipset), a macroprocessor, or other device for executing instructions. Theprocessor 205 includes a cache 270, which may include, but is notlimited to, an instruction cache to speed up executable instructionfetch, a data cache to speed up data fetch and store, and a translationlookaside buffer (TLB) used to speed up virtual-to-physical addresstranslation for both executable instructions and data. The cache 270 maybe organized as a hierarchy of more cache levels (L1, L2, and so on.).

The memory 210 may include one or combinations of volatile memoryelements (for example, random access memory, RAM, such as DRAM, SRAM,SDRAM) and nonvolatile memory elements (for example, ROM, erasableprogrammable read only memory (EPROM), electronically erasableprogrammable read only memory (EEPROM), programmable read only memory(PROM), tape, compact disc read only memory (CD-ROM), disk, diskette,cartridge, cassette or the like). Moreover, the memory 210 mayincorporate electronic, magnetic, optical, or other types of storagemedia. Note that the memory 210 may have a distributed architecture,where various components are situated remote from one another but may beaccessed by the processor 205.

The instructions in memory 210 may include one or more separateprograms, each of which comprises an ordered listing of executableinstructions for implementing logical functions. In the example of FIG.2, the instructions in the memory 210 include a suitable operatingsystem (OS) 211. The operating system 211 essentially may control theexecution of other computer programs and provides scheduling,input-output control, file and data management, memory management, andcommunication control and related services.

Additional data, including, for example, instructions for the processor205 or other retrievable information, may be stored in storage 220,which may be a storage device such as a hard disk drive or solid statedrive. The stored instructions in memory 210 or in storage 220 mayinclude those enabling the processor to execute one or more aspects ofthe systems and methods described herein.

The communication apparatus 200 may further include a display controller225 coupled to a user interface or display 230. In some embodiments, thedisplay 230 may be an LCD screen. In other embodiments, the display 230may include a plurality of LED status lights. In some embodiments, thecommunication apparatus 200 may further include a network interface 260for coupling to a network 265. The network 265 may be an IP-basednetwork for communication between the communication apparatus 200 and anexternal server, client and the like via a broadband connection. In anembodiment, the network 265 may be a satellite network. The network 265transmits and receives data between the communication apparatus 200 andexternal systems. In some embodiments, the network 265 may be a managedIP network administered by a service provider. The network 265 may beimplemented in a wireless fashion, for example, using wireless protocolsand technologies, such as WiFi, WiMax, satellite, or any other. Thenetwork 265 may also be a packet-switched network such as a local areanetwork, wide area network, metropolitan area network, the Internet, orother similar type of network environment. The network 265 may be afixed wireless network, a wireless local area network (LAN), a wirelesswide area network (WAN) a personal area network (PAN), a virtual privatenetwork (VPN), intranet or other suitable network system and may includeequipment for receiving and transmitting signals.

FIG. 5 illustrates a flowchart of an example method for correcting inputdata, according to one or more embodiments of the present invention. Theone or more operations illustrated is implemented by one or morecomponents of the data analysis system 100. FIG. 6 illustrates adata-flow when implementing the method in an example scenario, such asthe fitness tracking example scenario.

The method includes receiving time-series input data from the user 105,as shown at 505. The time-series input data includes one or more datastreams. The time-series input data includes data streams that the userenters manually. Alternatively, or in addition, the time-series inputdata includes data streams received automatically from one or moreuser-apparatus 110, such as wearable devices, smartphones, and the like.FIG. 6 illustrates examples of data-streams that may be input in thefitness tracking example scenario as user data 605. For example, thedata-streams may include time-series input data for the user'sdemographics, nutrition, activity, exercise, sleep, weight, medicalhistory, among others.

Referring back to FIG. 5, the data analysis system 100 pre-processes theinput data, as shown at 507. For example, as shown in FIG. 6, thepre-processing includes domain knowledge 607 and machine learningmethods to clean data 612, detect outliers 614, impute missing entries616, and process the data 618 from each data source. Such pre-processingand processing of the input data may be referred to as a stage 1. In oneor more examples, the pre-processing may be performed using domainknowledge 607 about the data-streams and/or the user-apparatus 110. Forexample, the domain knowledge 607 includes device accuracy, root-causedictionary, expert opinion, medical contexts, and the like. in one ormore examples, the cleaning data 612 includes deduplication, rule-basedelimination of infeasible values, and the like. Further, the outlierdetection includes identification of outliers for removal and/ornormalization via statistical clustering, nearest neighbor,classification, and the like. Further, imputing missing entries 616includes imputing missing data points in the input time-series databased on statistical learning methods such as, maximum likelihoodestimation (MLE), splines, regression, auto-regressive moving averagemodel with order n (timeframe), and the like. In addition, processing618 of the input data includes aggregation to level-of-detail generatinga prediction, feature extraction, and the like using the input data.Thus, the stage 1, or pre-processing includes adjusting the inputtime-series data or calibrating the data for using for generatingpredictions or insights by the data insight apparatus 130. In one ormore examples, the adjusted time-series data is stored in a datarepository of adjusted user-data 640.

Referring back to FIG. 5, the data analysis system 100 uses the inputdata to predict a measurable value for a future time-point, as shown at510. For example, referring to FIG. 6, the data analysis system uses adynamic system model 620 for combining the data streams from the inputdata to generate the prediction value, for example using algorithms likeKalman filtering, which iteratively learns coefficients and adjusts theprediction values in real-time to converge towards actual measuredvalue. For example, in case of the fitness tracking example scenario,the processing 618 includes predicting a weight of the user 105 at afuture time-point, and further adjusting the predicted values accordingto actual measured weight change measurements.

Referring back to FIG. 5, the actual measured values are received by thedata analysis system 100 at the future time-point, as shown at 520. Thedata analysis system 100 compares the actual measured values, such asthe weight, with the predicted value to check if the two match, as shownat 530. The predicted and measured values may be determined to matcheach other if the two values are within a predetermined threshold fromeach other. For example, the processed data from stage 1 is fed into astage 2, where the dynamic system model 620 is used to predict a futurevalue of a measureable value (for example, weight over time). In one ormore examples, the parameters of the model 620 are estimated using aKalman filter, and updates in real-time as more data is input by theuser 105.

Referring back to FIG. 5, if the predicted and measured values matcheach other, the data analysis system 100 continues to operate asdescribed. Alternatively, if the predicted and measured values do notmatch each other, the data analysis system 100 determines defect(s) inthe time-series input data, as shown at 540. For example, referring toFIG. 6, the data correction apparatus 120 performs the correction usinga root-cause determination 630. For example, classification algorithmsare used to identify the root-cause of the defects in the data anddiscrepancies between predicted and actual weight change. In one or moreexamples, the input time-series data is corrected using the identifieddefects, as shown at 550. Further, the data analysis system 100 predictsa revised measurable value using the revised time-series input data, asshown at 560. Referring to FIG. 6, the root-cause determination 630 isperformed using a personalized user model 650 that is unique to the user105. In one or more examples, the personalized user model 650 identifiesone or more defects associated with the data input from the user 105.For example, the personalized user model 650 identifies theuser-apparatus 110 that the user 105 employs to measure one or more ofthe data inputs. Alternatively, or in addition, the personalized usermodel 650 identifies that the user 105 over/under reports one or moredata inputs.

An example implementation of the dynamic system model 620 is nowdescribed in the context of the fitness tracking example scenario. Theexample implementation uses a Kalman filtering algorithm for aniterative estimation of weight to steer the prediction towards theactual measured results and to reduce prediction error. it should benoted that in other embodiments of the invention, the dynamic systemmodel 620 uses other LQE algorithms and/or implemented in domains otherthan the fitness tracking.

Consider the example scenario where the data analysis system 100receives as input time-series data for tracking fitness of the user 105,and predicts a change in weight for the user 105 using an energy balance(EB) equation. The EB equation describes a surplus or deficit ofcalories: EB=EI−EE, where EI=Energy Intake (food diary entries) andEE=Energy Expenditure (approximated by BMR, exercises, step counts).Here, body-mass ratio (BMR) is a function of height, weight, age,gender, and other such factors of the user 105. Based on the determinedEB, the data analysis system 100 identifies that if EB>0, the user 105experiences a weight gain, if EB<0, the user 105 experiences a weightloss, and in the case EB=0, the user 105 has a steady weight. Further,the data analysis system 100 uses domain knowledge 607 to determine thatfor the user 105 approximately 3500 calories is substantially equivalentto 1 lb of body mass, based on metabolism, genetics, body composition,diet, medical condition, and other such factors unique to the user 105.Thus, given the above model, and assuming accurate data input, the dataanalysis system 100 can predict weight-change over time given the dailyenergy balance.

However, such predictions are sensitive to accuracies/inaccuracies ofthe input data. For example, the user 105 can under/over report foodconsumption (EI), under/over report calories burned from exercise (EE).Alternatively, or in addition, wearable device measurements used by theuser 105 as input can be inaccurate. For example, step counts reportedare too high or low. Alternatively, or in addition, approximationequations can be wrong. For example, BMR is estimated to be too high, orcan be affected by medical conditions. Alternatively, or in addition,calorie estimates from exercise can be wrong, resulting in defecting EEinput. Thus, with such defective data being input to the data analysissystem, the predicted weight values at future time-points do not matchactual weight values measured when the future time-points occur. FIG. 7depicts an example deviation between predicted and measured values. Forexample, the predicted weight values 710 do not match the measuredweight values 720 as the days progress. Further, a prediction error 730,which is a difference between the predicted values 710 and measuredvalues 720, keeps increasing in value over time. Thus, the data analysissystem 100 is unable to generate accurate predictions because of thedefects in the input data.

The data correction apparatus 120 facilitates the data analysis system100 to address the above described technical challenge by identifyingthe defects in the input data, determining the root causes of thedefects, and further facilitating correction of the input data. Forexample, continuing the above example scenario, the linear dynamicalsystem model 620 using Kalman filtering can be mathematically expressedas in Table 1. The dynamical system model 620 includes theunderlying/internal state variables (x) that are determined based on thetime-series input variables (u), and noise corresponding to thefluctuations/variability in random state variables (q). For the weighttracking example, each time-point t, x_(1, t) represents energy intakeEI, x_(2, t) represents energy expenditure EE, and x_(3, t) representsthe weight predicted. The EI, EE, are calculated using the inputvariables u_(1, t) representing food consumed, u_(2, t) representinguser activity, and u_(3, t) representing user exercise, for example.Correspondingly, q_(1, t) represents noise in energy generated; q_(2, t)represents noise in energy spent; and q_(3, t) represents noise inweight.

TABLE 1 [ x 1 , t + 1 x 2 , t + 1 x 3 , t + 1 ]  x t + 1 = [ 1 α 1 α 2β 1 1 β 2 1 - 1 1 ]  α   [ x 1 , t x 2 , t x 3 , t ]  x t + [ 1 0 00 1 1 0 0 0 ]    [ u 1 , t u 2 , t u 3 , t ]  u t + [ q 1 , t q 2 ,t q 3 , t ]  q t

The dynamic system model 620 can be further expressed in matrix form asshown in Table 2.

TABLE 2 x_(t + 1) = _(t) + u_(t) + q_(t), where q_(t) ~

{q _(t),

_(t)} x₀ ~

{x ₀, Σ₀}, initial condition

 = Systems Matrix

 = Input Matrix

_(t) = Input noise Covariance Σ₀ = Initial Covariance

Further, the observed or measured values (y) are determined based on thebased on the time-series input variables (u), and noise corresponding tothe inaccuracies in measurements/recordings (r), and can bemathematically expressed as in Table 3, and in matrix form as depictedin Table 4.

TABLE 3 $\underset{\underset{y_{t}}{}}{\begin{bmatrix}y_{1,t} \\y_{2,t} \\\begin{matrix}y_{3,t} \\y_{4,t}\end{matrix}\end{bmatrix}} = {{\underset{\underset{}{}}{\begin{bmatrix}0 & 0 & 1 \\0 & 0 & 0 \\0 & 0 & 0 \\0 & 0 & 0\end{bmatrix}}\; \underset{\underset{x_{t}}{}}{\begin{bmatrix}x_{1,t} \\x_{2,t} \\x_{3,t}\end{bmatrix}}} + {\underset{\underset{}{}}{\begin{bmatrix}0 & 0 & 0 \\1 & 0 & 0 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix}}\; \underset{\underset{u_{t}}{}}{\begin{bmatrix}u_{1,t} \\u_{2,t} \\u_{3,t}\end{bmatrix}}} + \underset{\underset{r_{t}}{}}{\begin{bmatrix}r_{1,t} \\r_{2,t} \\r_{3,t}\end{bmatrix}}}$

TABLE 4 y t = t +  u t + r t ,  where r_(t) ~

 {r _(t),

_(t)}

 = Observations/Output Matrix

 = Feed-forward Matrix

_(t) = Measurement noise Covariance

In one or more examples, the data analysis system 100 learns the modelmatrices A, B, C, D and the noise covariance matrices Q, R, and Σ₀ usingdata-driven approach, for example, minimizing least-square errors.Further, the data analysis system 100 uses machine learning algorithms,such as Kalman filter, to estimate the state variables (x) and predictthe output for future time-points of the dynamical model system 620.

FIG. 8 illustrates predicting values at future time-points using thedynamic system model 620, according to one or more embodiments of thepresent invention. The dynamic system model 620 and a Kalman filteralgorithm module 810 receive the input data (u). In one or moreexamples, the Kalman filter algorithm module 810 is an implementation bythe data correction apparatus 120. Alternatively, or in addition, theKalman filter algorithm module 810 is an electronic circuit, or othersuch component of the data correction apparatus 120. In one or moreexamples, the input data is the adjusted data obtained afterpre-processing the data from the user 105. The output (y) from thedynamic system model 620 are input to the Kalman filter module 810. TheKalman filter module 810 determines an estimate of the state variables(x), and further determines an error between the predicted statevariables and the actual/measured values (y). Further, the Kalman filtermodule 810 further predicts the state variables for a future time pointt+1 as shown in Table 5.

TABLE 5 x ^ t  t = x ^ t  t - 1 + t  ( y t - (  x ^ t  t - 1 +  ut )  y ^ t )   x ^ t + 1  t =  x ^ t  t +  u t ,  where{circumflex over (x)}_(t|t) = Estimate of x_(t) at time t. {circumflexover (x)}_(t+1|t) = Prediction of x_(t+1) at time t.

_(t) = Kalman Gain.

In the fitness tracking example, the output prediction values for theweight from the dynamic system model 620 and the Kalman filter module810 are computed as ∈_(t)=y_(t)−ŷ_(t)˜

(μ_(t), Σ_(t)) where μ_(t) is prediction error based on q and r, andΣ_(t) is prediction error covariance. For example, the data correctionapparatus 120 compares the theoretical and real time empirical values ofμ_(t) and Σ_(t). If distribution of the errors do not match, then thecovariance matrix is used to check which of the observation variables(y) deviates from the predicted values (x).

For example, if the food intake input u_(t) values predicted by theKalman filter module 810 differ from the user input values y_(z) of thefood intake, the data correction apparatus 120 determines root-causesfor the difference. In one or more examples, the data correctionapparatus 120 provides confidence scores for one or more root-causesthat lead to the deviation. The user 105 selects the root-cause from thelist.

FIG. 9 illustrates a flowchart of an example method for determining ifthe input data contains defects, according to one or more embodiments ofthe present invention. In one or more examples, the operations of thismethod are implemented as part of block 540 of FIG. 5. The datacorrection apparatus 120 implements the method. The data correctionapparatus 120 computes the state variables, such as the energy intake,energy expensed based on the past state of the dynamic state model 620using the Kalman filter module 810, as shown at 910. For example, asdescribed herein, the Kalman filter module 810 computes X_(t) based onX_(t-1). The data correction apparatus 120 further computes a predictedvalue, for example weight, based on the computed state variables fromthe Kalman filter module 810, as shown at 920. For example, the datacorrection apparatus 120 computes Ŷ_(t) based on X_(t). In addition, thedata insight apparatus 130 computes a predicted value (second predictedvalue) based on user input data using dynamic system model 620, as shownat 930. For example, the data insight apparatus 130 computes Y_(t) basedon the input variables U_(t).

Further, the data correction apparatus 120 compares and computes adifference (or an error) between the two separate prediction values, afirst computed based on past state of the state variables from theKalman filter module 810, and a second computed based on user inputdata, as shown at 940. For example, the data correction apparatus 120computes ε_(t)=Y_(t)−Ŷ_(t).

The data correction apparatus 120 checks if a distribution of ε_(t)matches Gaussian distribution of error estimations of noise covariancematrices Q and R, as shown at 950. The Q and R noise covariance matricesrepresent the inaccuracies in the state variable calculations and inputdata recording respectively. In one or more examples, the distributionmatch is determined by comparing statistical parameters of the vectors,such as mean, covariance, or the like. If the distributions of ε_(t) andQ, R matrices match, the data correction apparatus 120 deems that theinput data from the user 105 is not defective, as shown at 960. If thedistributions do not match, the data correction apparatus 120 deems thatthe input data from the user 105 is defective, as shown at 970.

FIG. 10 illustrates a flowchart of an example method for detecting aroot-cause of the defect identified in the input data, according to oneor more embodiments of the present invention. The data correctionapparatus 120 compares the identified defect in the input data withroot-cause classification models, as shown in 1020. In one or moreexamples, the comparison is performed using artificial neural networks,or other machine learning techniques. In one or more examples, the datacorrection apparatus 120 builds the root-cause classification models, asshown at 1010. In one or more examples, the root-cause classificationmodels are built prior to the first use of the data analysis system 100,and further the data correction apparatus 120 maintains and continuouslyupdates the models.

For example, building the models includes building taxonomy of defectlabels, as shown at 1010-1. The labels may be specific to the input databeing tracked. For example, in the fitness tracking example, the labelsinclude under/over-reporting of food EI, under/over-reporting ofexercise EE, caloric expenditure estimation error, wearable deviceerror, and so on. In one or more examples, curated training data setwith such labels assigned to samples are created, as shown at 1010-2. Inone or more examples, any feedback received from the user 105 during theuse of the data analysis system 100 is stored in the training data set.For example, if the user 105 identifies that a specific entry is anoverreporting of EE, that entry and the corresponding label is includedin the training data set.

Further, the method includes training one or more classifiers using thecurated training data using classification algorithms such as logisticregression, decision trees, neural networks, Support Vector Machines(SVN), and the like, as shown at 1010-3. The classification models arevalidated, as shown at 1010-4. In one or more examples, the validationis performed using cross-validation of one or more classifiers, or byproducing probability distribution across data problems. In one or moreexamples, the validation is performed by displaying results to the user105, and receiving validation or correction of results from the user105, via the user-interface.

The method further includes updating the training data using the userfeedback, as shown at 1010-5. For example, the updating includesupdating a label data in the training dataset according to userfeedback. In one or more examples, a separate training set is formedusing the user feedback. The classifiers are further trained using theuser feedback.

Referring back to FIG. 10, the method of determining the root-causefurther includes presenting the user 105 with likelihoods of one or moreroot-causes that are identified, as shown at 1030.

FIG. 11 depicts an example user-interface generated by the datacorrection apparatus 120 according to one or more embodiments of thepresent invention. For example, the data correction apparatus 120displays a user-interface screen 1110 that includes the likelihoods ofone or more root-causes that are identified. In one or more examples,the likelihoods of the one or more labels (or root-causes) in thetraining data set are displayed. In one or more examples, thelikelihoods are depicted in the form of a graph. It should be noted thatin other examples, the likelihoods may be presented to the user 105 indifferent forms than shown here, such as pie-chart, text, and the like.Further, the label with the highest likelihood is selected as theroot-cause, as depicted in FIG. 11.

In one or more examples, the user 105 can select one of the root-causesfrom the user-interface 1110, irrespective of the likelihoods determinedby the data correction apparatus 120, as shown at 1040. For example, thedata correction apparatus 120 may determine that the data is beingunderreported based on the calculated likelihoods, although actually thedevice being used by the user 105 may be experiencing some malfunction.Accordingly, the user 105 may select the device error root-cause,overriding the determination by the data correction apparatus 120. Theselected label is then updated in the training data set.

Further, referring back to FIG. 5, after determining the defective dataand the corresponding root-cause, the user 105 is presented thedefective time-series input data for revision. FIG. 12 depicts exampleuser-interface generated by the data correction apparatus 120 accordingto one or more embodiments of the present invention. For example, thedata correction apparatus displays the user-interface screen 1210 thatindicates the recorded data 1214 that is determined to be defective, theroot-cause of the defect 1212, and an estimated data entry 1216 toreplace the defective data. The estimate is computed by the Kalmanfilter module 810. The data correction apparatus further includes one ormore user-interactive elements to facilitate the user 105 to providefeedback/revisions. For example, the data correction apparatus 120includes on the user-interface 1210 a user-interactive element 1220 thatfacilitates the user 105 to confirm the recorded data 1214 as accurate.Alternatively, or in addition, the user-interface 1210 includes auser-interactive element 1230 that facilitates the user 105 toreplace/revise the recorded input data 1214 with the estimated value1216. In one or more examples, the user interface 1210 includes auser-interactive element 1240 that facilitates the user 105 to edit therecorded input value 1214 manually, for example to new value that isdifferent than the estimated value 1216. The user-interface 1210 thusfacilitates the user 105 to revise the input time-series data. It shouldbe noted that the user-interface 1210 depicted is one example, and thatin other examples the data correction apparatus 120 generates differentuser-interface screens that include additional, fewer, and/or differentuser-interface elements than those displayed in FIG. 12.

Referring back to FIG. 5, the data analysis system 100 uses the revisedinput data to generate a new prediction, as shown in 560. In one or moreexamples, the new prediction is presented to the user 105. FIG. 13depicts an example user-interface 1310 according to one or moreembodiments of the present invention. For example, the user-interface1310 presents the old prediction values 1320 based on the defectiveinput data, as well as the new prediction values 1330 based on therevised, corrected input data. In one or more examples, such a visualdepiction of the two predictions facilitates the user 105 to revisit thecorrections s/he may have done earlier.

Thus, the technical solutions described herein facilitate a system toclean and analyze data, and further generate predictions and insightsbased on the data. In one or more examples, the cleaning stagefacilitates removing detectable erroneous data, removing/fixingoutliers, and imputing missing data in the input data. Further, thesystem facilitates performing data transformations used for generatingfuture prediction using machine learning methods. In one or moreexamples, cleaning the data includes rule-based criteria establishedfrom domain knowledge, published literature, and expert opinion.Alternatively, or in addition, outlier detection and remediation isperformed via statistical clustering, nearest neighbor or classificationalgorithms. Alternatively, or in addition, imputation of missing data isperformed via statistical learning methods such maximum likelihoodestimation, splines, regression, and Auto-Regressive Moving Averagemodels. Further yet, in one or more examples, the processing algorithmaggregates the data to a sufficient level-of-detail required in futuresteps and extracts features from the input data that are curated bydomain knowledge including expert opinion and published literature.

Further, the system employs and implements a dynamic system model topredict future value(s) of one of the data types of interest to theend-user, for example using machine learning techniques. For example,the input data is applied to a predictive model that is represented as adynamical system of equations.

Further yet, the system includes a module or subsection that utilizesLQE algorithms such as Kalman filter to improve the prediction abilityof the aforementioned model and further to estimate model parameters.For example, the parameters of the predictive model are calibrated viathe Kalman filter approach.

Further yet, the system includes a root-cause analysis module/subsectionthat uses machine learning classification algorithms (kNN, clustering,neural networks, etc.) to determine which errors exist in the input dataand predictions and to classify the likelihood of these errors. In oneor more examples, the data and features generated by the predictivemodel, the parameters of the prediction model generated using the Kalmanfiltering, and domain knowledge and expert opinion are used to train amachine learning classification model to identify the likelihood of dataand prediction errors. In one or more examples, the contents of theresults of the root-cause analysis include an updated prediction, andthe root-cause error classifications, and prompts asking the user toconfirm or edit the suspected data errors.

In one or more examples, the data used as input data is received fromconsumer devices such as activity trackers, smart watches, or smartscales, and entered manually by an end-user via a smartphone, web-basedapplication, and the like. For example, in one or more examples,displaying of results can occur through prompts in a smartphone orweb-based application and prompts the user to confirm or edit theerroneous/defective input data. In one or more examples, the user inputis stored, used to label the user data, and fed back to theclassification algorithm to improve classification accuracy.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a processor, a time-series data input by a user;computing, by the processor, a predicted value based on the time-seriesdata; and determining, by the processor, a defect in the time-seriesdata input by the user based on a difference between the predicted valueand a measured value.
 2. The computer-implemented method of claim 1,further comprising determining, by the processor, a cause of the defectin the time-series data automatically by using a machine learningalgorithm.
 3. The computer-implemented method of claim 2, furthercomprising displaying, by the processor, a prompt for the user, theprompt displaying the cause of the defect.
 4. The computer-implementedmethod of claim 2, further comprising computing, by the processor, anestimated time-series data for the user.
 5. The computer-implementedmethod of claim 4, further comprising displaying, by the processor, aprompt for the user, the prompt displaying the cause of the defect andthe estimated time-series data to be used instead of the time-seriesdata input by the user.
 6. The computer-implemented method of claim 5,further comprising, in response to the user selecting the estimatedtime-series data: computing, by the processor, a revised predicted valuebased on the estimated time-series data; and displaying, by theprocessor, the revised predicted value.
 7. The computer-implementedmethod of claim 2, further comprising determining that the cause of thedefect is one from a group of causes consisting of the userunder-reporting the time-series data, the user over-reporting thetime-series data, and a sensor malfunction.
 8. A system comprising: amemory; and a processor coupled with the memory, the processorconfigured to: receive a time-series data input by a user; compute apredicted value based on the time-series data; and determine a defect inthe time-series data input by the user based on a difference between thepredicted value and a measured value.
 9. The system of claim 8, theprocessor configured to determine a cause of the defect in thetime-series data automatically by using a machine learning algorithm.10. The system of claim 9, the processor further configured to display aprompt for the user, the prompt displaying the cause of the defect. 11.The system of claim 9, the processor further configured to compute anestimated time-series data for the user.
 12. The system of claim 11, theprocessor further configured to display a prompt for the user, theprompt displaying the cause of the defect and the estimated time-seriesdata to be used instead of the time-series data input by the user. 13.The system of claim 12, the processor further configured to, in responseto the user selecting the estimated time-series data: compute a revisedpredicted value based on the estimated time-series data; and display therevised predicted value.
 14. The system of claim 9, the processorfurther configured to determine that the cause of the defect is one froma group of causes consisting of the user under-reporting the time-seriesdata, the user over-reporting the time-series data, and a sensormalfunction.
 15. A computer program product for correcting input datathe computer program product comprising a computer readable storagemedium having program instructions embodied therewith, the programinstructions executable by a processing circuit to cause the processingcircuit to: receive a time-series data input by a user; compute a firstpredicted value based on the time-series data input by the user; computea second predicted value based on an estimated time-series data, theestimated time-series data computed based on the time-series data inputby the user; and determine a defect in the time-series data input by theuser based on a difference between the first predicted value and thesecond predicted value.
 16. The computer program product of claim 15,the program instructions further executable to cause the processingcircuit to: display a prompt for the user, the prompt displaying thecause of the defect, and the estimated time-series data to be usedinstead of the time-series data input by the user.
 17. The computerprogram product of claim 16, the program instructions further executableto cause the processing circuit to determine that the cause of thedefect is one from a group of causes consisting of the userunder-reporting the time-series data, the user over-reporting thetime-series data, and a sensor malfunction.
 18. The computer programproduct of claim 15, wherein the estimated time-series data is computedusing Kalman filtering.
 19. A computer-implemented method comprising:receiving, by a processor, a time-series data input by a user;computing, by the processor, a first plurality of predicted values basedon the time-series data input by the user; computing, by the processor,a second plurality of predicted values by: determining estimatedtime-series data based on the time-series data input by the user; andcomputing the second plurality of predicted values based on theestimated time-series data; and determining, by the processor, a defectin the time-series data input by the user based on a distribution of aplurality of differences between respective values from the firstplurality of predicted values and the second plurality of predictedvalues.
 20. The computer-implemented method of claim 19, whereindetermining the defect in the time-series based on the distribution ofthe plurality of differences comprises determining if the distributionis Gaussian.
 21. The computer-implemented method of claim 19, furthercomprising determining, by the processor, a cause of the defect in thetime-series data automatically by using a machine learning algorithmbased on the distribution of the plurality of differences.
 22. Thecomputer-implemented method of claim 21, further comprising displaying,by the processor, a prompt for the user, the prompt displaying the causeof the defect and the estimated time-series data to be used instead ofthe time-series data input by the user.
 23. The computer-implementedmethod of claim 22, further comprising, in response to the userselecting the estimated time-series data: computing, by the processor, arevised predicted value based on the estimated time-series data; anddisplaying, by the processor, the revised predicted value.
 24. A systemcomprising: a memory; and a processor coupled with the memory, theprocessor configured to: receive a time-series data input by a user;compute a first plurality of predicted values based on the time-seriesdata input by the user; compute a second plurality of predicted valuesby: determining estimated time-series data based on the time-series datainput by the user; and computing the second plurality of predictedvalues based on the estimated time-series data; and determine a defectin the time-series data input by the user based on a distribution of aplurality of differences between respective values from the firstplurality of predicted values and the second plurality of predictedvalues.
 25. The system of claim 24, the processor further configured to:determine a cause of the defect in the time-series data automatically byusing a machine learning algorithm based on the distribution of theplurality of differences; display a prompt for the user, the promptdisplaying the cause of the defect and the estimated time-series data tobe used instead of the time-series data input by the user; and inresponse to the user selecting the estimated time-series data: compute arevised predicted value based on the estimated time-series data; anddisplay the revised predicted value.